Do early effects of predictability in visual word recognition reflect prediction error? Electrophysiological research investigating word processing has demonstrated predictability effects in the N1, or first negative component of the event-related potential (ERP). However, findings regarding the magnitude of effects and potential interactions of predictability with lexical variables have been inconsistent. Moreover, past studies have typically used categorical designs with relatively small samples and relied on by-participant analyses. Nevertheless, reports have generally shown that predicted words elicit less negative-going (i.e., lower amplitude) N1s, a pattern consistent with a simple predictive coding account. In our preregistered study, we tested this account via the interaction between prediction magnitude and certainty. A picture-word verification paradigm was implemented in which pictures were followed by tightly matched picture-congruent or picture-incongruent written nouns. The predictability of target (picture-congruent) nouns was manipulated continuously based on norms of association between a picture and its name. ERPs from 68 participants revealed a pattern of effects opposite to that expected under a simple predictive coding framework.

Journal Article Efficient Sampling and Reconstruction Strategies for in-situ SEM/STEM Get access N D Browning, N D Browning Mechanical, Materials, & Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, UKPhysical & Computational Science, Pacific Northwest National Lab, Richland, WA 99352, USASivananthan Laboratories, 590 Territorial Drive, Bolingbrook, IL 60440, USA Search for other works by this author on: Oxford Academic Google Scholar M Bahri, M Bahri Mechanical, Materials, & Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar J Castagna, J Castagna UKRI-STFC Hartree Centre, Daresbury Laboratory, Warrington, WA4 4AD, UK Search for other works by this author on: Oxford Academic Google Scholar K Chen, K Chen Department of Mathematical Sciences, University of Liverpool, Liverpool, L79 7ZL, UK Search for other works by this author on: Oxford Academic Google Scholar B L Mehdi, B L Mehdi Mechanical, Materials, & Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar D Nicholls, D Nicholls Mechanical, Materials, & Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar W Pearson, W Pearson Distributed Algorithms CDT, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar A W Robinson, A W Robinson Mechanical, Materials, & Aerospace Engineering, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar J Taylor, J Taylor Distributed Algorithms CDT, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar J Wells, J Wells Distributed Algorithms CDT, University of Liverpool, Liverpool, L69 3GH, UK Search for other works by this author on: Oxford Academic Google Scholar ... Show more Y Zheng Y Zheng Department of Eye and Vision Science, University of Liverpool, Liverpool. L7 8TX, UK Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 1878–1879, https://doi.org/10.1017/S1431927622007371 Published: 01 August 2022

Journal Article Deep Learning-based Blind Denoising for Enhancing Energy-dispersive X-ray Spectroscopy (EDS) Images Get access Jack Taylor, Jack Taylor Distributed Algorithms CDT, University of Liverpool, Liverpool, UK Search for other works by this author on: Oxford Academic Google Scholar Ke Chen, Ke Chen Department of Mathematical Sciences, University of Liverpool, Liverpool, UK Search for other works by this author on: Oxford Academic Google Scholar Yalin Zheng, Yalin Zheng Department of Eye and Vision Science, University of Liverpool, Liverpool, UK Search for other works by this author on: Oxford Academic Google Scholar Nigel D Browning Nigel D Browning Department of Mechanical, Materials, and Aerospace Engineering, University of Liverpool, Liverpool, UKPhysical and Computational Sciences, Pacific Northwest National Lab, Richland, WA, USASivananthan Laboratories, Inc., Bolingbrook, Illinois, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 28, Issue S1, 1 August 2022, Pages 508–509, https://doi.org/10.1017/S1431927622002665 Published: 01 August 2022

Embodied cognition theories propose that abstract concepts are grounded in a variety of exogenous and endogenous experiences which may be flexibly activated across contexts and tasks. In three experiments, we explored how semantic size (i.e., the magnitude, dimension or extent of an object or a concept) of abstract (vs. concrete) concepts is mentally represented. We show that abstract size is metaphorically associated with the physical size of concrete objects (Experiment 1) and can produce a semantic-font size congruency effect comparable to that demonstrated in concrete words during online lexical processing (Experiment 2). Critically, this size congruency effect is large when a word is judged by its semantic size but significantly smaller when it is judged by its emotionality (Experiment 3), regardless of concreteness. Our results suggest that semantic size of abstract concepts can be grounded in visual size, which is activated adaptively under different task demands. The present findings advocate flexible embodiment of semantic representations, with an emphasis on the role of task effects on conceptual processing.

Studies which provide norms of Likert ratings typically report per-item summary statistics. Traditionally, these summary statistics comprise the mean and the standard deviation (SD) of the ratings, and the number of observations. Such summary statistics can preserve the rank order of items, but provide distorted estimates of the relative distances between items because of the ordinal nature of Likert ratings. Inter-item relations in such ordinal scales can be more appropriately modelled by cumulative link mixed effects models (CLMMs). In a series of simulations, and with a reanalysis of an existing rating norms dataset, we show that CLMMs can be used to more accurately norm items, and can provide summary statistics analogous to the traditionally reported means and SDs, but which are disentangled from participants’ response biases. CLMMs can be applied to solve important statistical issues that exist for more traditional analyses of rating norms.

Particle Filters (PFs) are Sequential Monte Carlo methods which are widely used to solve filtering problems of dynamic models under Non-Linear Non-Gaussian noise. Modern PF applications have demanding accuracy and run-time constraints that can be addressed through parallel computing. However, an efficient parallelization of PFs can only be achieved by effectively parallelizing the bottleneck: resampling and its constituent redistribution step. A pre-existing implementation of redistribute on Shared Memory Architectures (SMAs) achieves O(N/T log(2)N) time complexity over T parallel cores. This redistribute implementation is, however, highly computationally intensive and cannot be effectively parallelized due to the inherently limited number of cores of SMAs. In this paper, we propose a novel parallel redistribute on OpenMP 4.5 which takes O(N/T + log(2)N) steps and fully exploits the computational power of SMAs. The proposed approach is up to six times faster than the O(N/T log(2)N) one and its implementation on GPU provides a further three-time speed-up vs its equivalent on a 32-core CPU. We also show on an exemplary PF that our redistribution is no longer the bottleneck.

LexOPS is an R package and user interface designed to facilitate the generation of word stimuli for use in research. Notably, the tool permits the generation of suitably controlled word lists for any user-specified factorial design and can be adapted for use with any language. It features an intuitive graphical user interface, including the visualization of both the distributions within and relationships among variables of interest. An inbuilt database of English words is also provided, including a range of lexical variables commonly used in psycholinguistic research. This article introduces LexOPS, outlining the features of the package and detailing the sources of the inbuilt dataset. We also report a validation analysis, showing that, in comparison to stimuli of existing studies, stimuli optimized with LexOPS generally demonstrate greater constraint and consistency in variable manipulation and control. Current instructions for installing and using LexOPS are available at https://JackEdTaylor.github.io/LexOPSdocs/..

Plasmonic nanoparticles (NPs), predominantly gold (AuNPs), are easily internalised into cells and commonly employed as nanosensors for reporter-based and reporter-free intracellular SERS applications. While AuNPs are generally considered non-toxic to cells, many biological and toxicity studies report that exposure to NPs induces cell stress through the generation of reactive oxygen species (ROS) and the upregulated transcription of pro-inflammatory genes, which can result in severe genotoxicity and apoptosis. Despite this, the extent to which normal cellular metabolism is affected by AuNP internalisation remains a relative unknown along with the contribution of the uptake itself to the SERS spectra obtained from within so called ‘healthy’ cells, as indicated by traditional viability tests. This work aims to interrogate the perturbation created by treatment with AuNPs under different conditions and the corresponding effect on the SERS spectra obtained. We characterise the changes induced by varying AuNP concentrations and medium serum compositions using biochemical assays and correlate them to the corresponding intracellular reporter-free SERS spectra. The different serum conditions lead to different extents of nanoparticle internalisation. We observe that changes in SERS spectra are correlated to an increasing amount of internalisation, confirmed qualitatively and quantitatively by confocal imaging and ICP-MS analysis, respectively. We analyse spectra and characterise changes that can be attributed to nanoparticle induced changes. Thus, our study highlights a need for understanding condition-dependent NP-cell interactions and standardisation of nanoparticle treatments in order to establish the validity of intracellular SERS experiments for use in all arising applications.

Surface-enhanced Raman spectrocopy (SERS) offers ultrasensitive vibrational fingerprinting at the nanoscale. Its non-destructive nature affords an ideal tool for interrogation of the intracellular environment, detecting the localisation of biomolecules, delivery and monitoring of therapeutics and for characterisation of complex cellular processes at the molecular level. Innovations in nanotechnology have produced a wide selection of novel, purpose-built plasmonic nanostructures capable of high SERS enhancement for intracellular probing while microfluidic technologies are being utilised to reproducibly synthesise nanoparticle (NP) probes at large scale and in high throughput. Sophisticated multivariate analysis techniques unlock the wealth of previously unattainable biomolecular information contained within large and multidimensional SERS datasets. Thus, with suitable combination of experimental techniques and analytics, SERS boasts enormous potential for cell based assays and to expand our understanding of the intracellular environment. In this review we trace the pathway to utilisation of nanomaterials for intracellular SERS. Thus we review and assess nanoparticle synthesis methods, their toxicity and cell interactions before presenting significant developments in intracellular SERS methodologies and how identified challenges can be addressed.